An Orthotropic Integrated Flow-Stress Model for Process Simulation of Composite Materials—Part I: Two-Phase Systems
Bibliographic record
Abstract
An integrated flow-stress (IFS) model provides a seamless and mechanistic connection between the two distinct regimes during the manufacturing process of composite materials, namely, fluid flow in the pregelation stage of the thermoset resin and stress development in the composite when it acts as a solid material. In this two-part paper, the two- and three-phase isotropic IFS models previously developed by the authors are extended to the general case of composite materials with orthotropic constituents. Part I presents the two-phase, fluid-solid, orthotropic model formulation for the case where the fluid phase solidifies during the course of curing. Part II extends the orthotropic formulation to a three-phase model that includes a gas phase as the third constituent of the composite material system. A broader definition of material properties in poroelasticity formulation is adopted in the development of the general orthotropic formulation. The model is implemented in a two-dimensional (2D) plane strain u-v-P finite element (FE) code and its capability in predicting the flow-compaction behavior and stress development is demonstrated through application to a case study involving an L-shaped unidirectional laminate undergoing curing on a conforming convex tool. Comparison of the results with those obtained from sole modeling of the stress development reveals the importance of capturing the simultaneous and interactive effect of the mechanisms involved during the entire process cycle using an IFS modeling approach presented in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".